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344249

The classification of lung cancer using deep learning Techniques

Article

Last updated: 09 Mar 2025

Subjects

-

Tags

(Mathematics and Computer Science) - (Bioinformatics)

Abstract

Lung Cancer Classification using Convolutional Neural Networks (CNN) has emerged as a critical research endeavor in medical imaging, holding profound implications for early diagnosis and effective treatment. The accurate categorization of lung cancer plays a pivotal role in enhancing patient outcomes and reducing mortality rates. This study presents a comprehensive study leveraging the power of CNNs to achieve robust and high-performing lung cancer classification. The study capitalizes on two distinct datasets, comprising 1097 and 364 lung images, respectively. The methodological progression unfolds with meticulous data scaling, followed by a judicious 80:10:10 data split to facilitate model training, validation, and testing. To address the class imbalance, an innovative approach utilizing Synthetic Minority Over-sampling Technique (SMOTE) is employed, bolstering the learning process on both training and validation sets. The crux of the study lies in the meticulously designed CNN architecture, boasting a stratified composition of 9 layers. Anchored by the quintessential convolutional layers, the model adeptly captures intricate hierarchical features inherent to the input 2D lung images. These acquired representations are seamlessly channeled through dense layers, culminating in the accurate and confident classification of each image into its respective class. The experimental outcomes underscore the potency of the proposed approach, with the first model yielding an impressive accuracy of 99.1%, while the second dataset achieves remarkable perfection with a 100% accuracy rate. This research serves as a significant stride towards the realization of a reliable and efficient tool for lung cancer classification, holding promise for early detection and personalized medical interventions.

DOI

10.21608/aels.2024.270817.1047

Keywords

Keywords: Convolutional Neural Networks (CNN), Medical Imaging, early diagnosis, Class Imbalance, Synthetic Minority Over-sampling Technique (SMOTE)

Authors

First Name

Ola

Last Name

Khedr

MiddleName

Salah

Affiliation

Department of Mathematics - Computer Sciences, Faculty of Science, Suez Canal University, Ismailia, 41552, Egypt

Email

ola_salah@science.suez.edu.eg

City

-

Orcid

-

First Name

Mohamed

Last Name

Wahed

MiddleName

-

Affiliation

Department of Computer Sciences, Faculty of Computers and Informatics, Suez Canal University, Ismailia, 41552, Egypt

Email

drmoh@ci.suez.edu.eg

City

-

Orcid

-

First Name

Al-Sayed

Last Name

Al-Attar

MiddleName

-

Affiliation

Department of Pathology, Faculty of Veterinary medicine, Zagazig University, Zagazig, 11144, Egypt

Email

arabdelmegeed@vet.zu.edu.eg

City

-

Orcid

-

First Name

Entsar

Last Name

Abdel-Rehim

MiddleName

Ahmed

Affiliation

Department of Mathematics - Computer Sciences, Faculty of Science, Suez Canal University, Ismailia, 41552, Egypt

Email

entsar_abdalla@science.suez.edu.eg

City

-

Orcid

-

Volume

5

Article Issue

2

Related Issue

54223

Issue Date

2024-03-01

Receive Date

2024-02-17

Publish Date

2024-03-01

Page Start

21

Page End

33

Print ISSN

2805-3060

Online ISSN

2805-3079

Link

https://aels.journals.ekb.eg/article_344249.html

Detail API

http://journals.ekb.eg?_action=service&article_code=344249

Order

2

Type

Original research articles

Type Code

2,116

Publication Type

Journal

Publication Title

Advances in Environmental and Life Sciences

Publication Link

https://aels.journals.ekb.eg/

MainTitle

The classification of lung cancer using deep learning Techniques

Details

Type

Article

Created At

09 Mar 2025